Lp-Norm Support Vector Regression with Feature Selection"> Lp-norm, sparse solution, feature selection, determinants of house price"> Lp-Norm Support Vector Regression with Feature Selection | Keywords: support vector regression, Lp-norm, sparse solution, feature selection, determinants of house price | Author: Ya-Fen Ye, Yuan-Hai Shao, and Chun-Na Li" /> JACIII Vol.19 p.407 (2015) | Fuji Technology Press: academic journal publisher
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JACIII Vol.19 No.3 pp. 407-416
doi: 10.20965/jaciii.2015.p0407
(2015)

Paper:

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Wavelet Lp-Norm Support Vector Regression with Feature Selection

Ya-Fen Ye*,**, Yuan-Hai Shao*, and Chun-Na Li*

*Zhijiang College, Zhejiang University of Technology
182 Zhijiang Road, Hangzhou 310024, China

**College of Economics, Zhejiang University
Hangzhou 310027, China

Received:
December 30, 2013
Accepted:
February 25, 2015
Published:
May 20, 2015
Creative Commons License
Keywords:
support vector regression, Lp-norm, sparse solution, feature selection, determinants of house price
Abstract
This paper proposes wavelet Lp-norm support vector regression (Lp-WSVR) to solve feature selection and regression problems effectively. Unlike conventional support vector regression (SVR), linear Lp-WSVR ensures that useful features are selected based on theoretical analysis. By using the wavelet kernel,Lp-WSVR approaches any curve in quadratic continuous integral space that leads to improving regression performance. Results of experiments show the superiority of Lp-WSVR in both feature selection and regression performances. Applying Lp-WSVR to Chinese real estate prices shows that the most significant and powerful factor contributing to Chinese housing prices is monetary growth.
Cite this article as:
Y. Ye, Y. Shao, and C. Li, “Wavelet Lp-Norm Support Vector Regression with Feature Selection,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.3, pp. 407-416, 2015.
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